{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probabilistic RNN\n",
    "In this notebook, we show an example of how a probabilistic RNN can be used with darts.\n",
    "This type of RNN was inspired by, and is almost identical to DeepAR: https://arxiv.org/abs/1704.04110"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shutil\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.models import RNNModel, ExponentialSmoothing, BlockRNNModel\n",
    "from darts.metrics import mape\n",
    "from darts.utils.statistics import check_seasonality, plot_acf\n",
    "import darts.utils.timeseries_generation as tg\n",
    "from darts.datasets import AirPassengersDataset, EnergyDataset\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "from darts.utils.missing_values import fill_missing_values\n",
    "from darts.utils.likelihood_models import GaussianLikelihood\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable noise series\n",
    "As a toy example we create a target time series that is created by taking the some of a sine wave and a gaussian noise series. To make things interesting, the intensity of the gaussian noise is also modulated by a sine wave (with a different frequency). This means that the effect of the noise gets stronger and weaker in an oscillating fashion. The idea is to test whether a probabilistic RNN can model this oscillating uncertainty in its predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 400\n",
    "trend = tg.linear_timeseries(length=length, end_value=4)\n",
    "season1 = tg.sine_timeseries(length=length, value_frequency=0.05, value_amplitude=1.0)\n",
    "noise = tg.gaussian_timeseries(length=length, std=0.6)\n",
    "noise_modulator = (\n",
    "    tg.sine_timeseries(length=length, value_frequency=0.02)\n",
    "    + tg.constant_timeseries(length=length, value=1)\n",
    ") / 2\n",
    "noise = noise * noise_modulator\n",
    "\n",
    "target_series = sum([noise, season1])\n",
    "covariates = noise_modulator\n",
    "target_train, target_val = target_series.split_after(0.65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_train.plot()\n",
    "target_val.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following we train a probabilistic RNN to predict the targets series in an autoregressive fashion, but also by taking into consideration the modulation of the noise component as a covariate which is known in the future. So the RNN has information on when the noise component of the target is severe, but it doesn't know the noise component itself. Let's see if the RNN can make use of this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cbc81b7423d49b4a0deebd494ac2034",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0447\r"
     ]
    }
   ],
   "source": [
    "my_model = RNNModel(\n",
    "    model=\"LSTM\",\n",
    "    hidden_dim=20,\n",
    "    dropout=0,\n",
    "    batch_size=16,\n",
    "    n_epochs=50,\n",
    "    optimizer_kwargs={\"lr\": 1e-3},\n",
    "    random_state=0,\n",
    "    training_length=50,\n",
    "    input_chunk_length=20,\n",
    "    likelihood=GaussianLikelihood(),\n",
    ")\n",
    "\n",
    "my_model.fit(target_train, future_covariates=covariates, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = my_model.predict(80, num_samples=50)\n",
    "target_val.slice_intersect(pred).plot(label=\"target\")\n",
    "pred.plot(label=\"prediction\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that, on top of correctly predicting the, granted, simple oscillatory behavior of the target, the RNN correctly expresses more uncertainty in its predictions when the noise component is higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily energy production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = EnergyDataset().load().pd_dataframe()\n",
    "df3_day_avg = (\n",
    "    df3.groupby(df3.index.astype(str).str.split(\" \").str[0]).mean().reset_index()\n",
    ")\n",
    "series_en = fill_missing_values(\n",
    "    TimeSeries.from_dataframe(\n",
    "        df3_day_avg, \"time\", [\"generation hydro run-of-river and poundage\"]\n",
    "    ),\n",
    "    \"auto\",\n",
    ")\n",
    "\n",
    "# convert to float32\n",
    "series_en = series_en.astype(np.float32)\n",
    "\n",
    "# scale\n",
    "scaler_en = Scaler()\n",
    "series_en_transformed = scaler_en.fit_transform(series_en)\n",
    "train_en_transformed, val_en_transformed = series_en_transformed.split_after(\n",
    "    pd.Timestamp(\"20170901\")\n",
    ")\n",
    "\n",
    "# add the day as a covariate\n",
    "day_series = datetime_attribute_timeseries(\n",
    "    series_en_transformed, attribute=\"day\", one_hot=True, dtype=np.float32\n",
    ")\n",
    "scaler_day = Scaler()\n",
    "day_series = scaler_day.fit_transform(day_series)\n",
    "train_day, val_day = day_series.split_after(pd.Timestamp(\"20170901\"))\n",
    "\n",
    "series_en_transformed.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_en = RNNModel(\n",
    "    model=\"LSTM\",\n",
    "    hidden_dim=20,\n",
    "    n_rnn_layers=2,\n",
    "    dropout=0.2,\n",
    "    batch_size=16,\n",
    "    n_epochs=10,\n",
    "    optimizer_kwargs={\"lr\": 1e-3},\n",
    "    random_state=0,\n",
    "    training_length=300,\n",
    "    input_chunk_length=300,\n",
    "    likelihood=GaussianLikelihood(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "240675b7978840d0b5136bc7e1743e57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.7328\r"
     ]
    }
   ],
   "source": [
    "model_en.fit(series=train_en_transformed, future_covariates=train_day, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70e9a2b724ba4db88d1bc05b70788a56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/83 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "backtest_en = model_en.historical_forecasts(\n",
    "    series=series_en_transformed,\n",
    "    future_covariates=day_series,\n",
    "    num_samples=50,\n",
    "    start=0.7,\n",
    "    forecast_horizon=30,\n",
    "    stride=5,\n",
    "    retrain=False,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "series_en_transformed[1000:].plot(label=\"actual\")\n",
    "backtest_en.plot(label=\"backtest (H=30)\", low_quantile=0.01, high_quantile=0.99)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
